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Adversarial Robust Model Compression Using In-Train Pruning., , , , , , , , , and 2 other author(s). CVPR Workshops, page 66-75. Computer Vision Foundation / IEEE, (2021)Mind the Scaling Factors: Resilience Analysis of Quantized Adversarially Robust CNNs., , , , , , , and . DATE, page 706-711. IEEE, (2022)Hardware-Software Co-Design of Deep Neural Networks: From Handcrafted to Automated Design and Deployment.. Technical University of Munich, Germany, (2022)Fine-Grained Power Modeling of Multicore Processors Using FFNNs., , , , and . SAMOS, volume 12471 of Lecture Notes in Computer Science, page 186-199. Springer, (2020)OrthrusPE: Runtime Reconfigurable Processing Elements for Binary Neural Networks., , , and . DATE, page 1662-1667. IEEE, (2020)BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices., , , , , and . IPDPS Workshops, page 108-115. IEEE, (2021)Wino Vidi Vici: Conquering Numerical Instability of 8-bit Winograd Convolution for Accurate Inference Acceleration on Edge., , , , , , , , , and 1 other author(s). WACV, page 53-62. IEEE, (2024)MATAR: Multi-Quantization-Aware Training for Accurate and Fast Hardware Retargeting., , , , , , , , , and . DATE, page 1-6. IEEE, (2024)WinoTrain: Winograd-Aware Training for Accurate Full 8-bit Convolution Acceleration., , , , , , , and . DAC, page 1-6. IEEE, (2023)Lightweight Instruction Set for Flexible Dilated Convolutions and Mixed-Precision Operands., , , , , , , and . ISQED, page 1-8. IEEE, (2023)